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Maestro v3.0 is out! Huge focus on H3 speed across GPUs + Flux3 & other goodies!Two weeks! That’s how long it’s been since I pushed an update. The longest since launch. And I’ve been grinding! Huge focus on H3 speed across GPUs, + Flux3 & other goodies! Maestro v3.0 Updates: • Faster generation and better memory management. Optimized kernels deliver roughly 1.5–2× speedups in our matched benchmarks, with major improvements on lower-VRAM machines. • Smarter Auto-Tune and longer clip recommendations. Settings and recommended durations account for GPU, VRAM, RAM, resolution and workload—including tested 720p Fused generation on 8GB GPUs. • Improved Director planning and prompt enhancement. Better story progression, character consistency and continuity, fewer unnecessary planning failures, and optional lyric guidance for music videos. • New image models. Flux 3 Klein Distilled and Base support generation and editing with up to 10 reference images. Qwen Image 2.1 Turbo is also available. • Visual Prompt Helper. Draw composition boxes, select reference-image regions and mark objects for removal to build clearer image-editing instructions. • Expanded H3 capabilities. 2× VAE upscaling, LightVAE and additional sparse-attention choices, including experimental Veda. • Fish Audio Drama 3. Expressive dialogue with emotions, whispers, laughter, pauses and voice cloning from up to three reference recordings. • More capable local LLM options. Underdog Saluki 27B joins the available writers, with optional experimental MTP acceleration for compatible models. It’s a highly optimized 8B model based on & benchmarks around qwen3.8 27B but works on smaller GPUs. • Better Director editing and regeneration. Load individual shots into Studio, retain prompt drafts through failed reruns, replace takes and rejoin the production. • A cleaner everyday interface. Models organized by media type and mode, simpler names and defaults, expandable Director clip groups, and video durations visible before playback.
Now Available: Drama 3 Offload Studio!Hello! Olá! Today I am excited to share my latest project Drama 3 Offload Studio for Pinokio: a complete 1-click local installer for the Fish Audio Drama 3, the brand new 35B TTS model that clones voices, designs new ones from a description, and does all sorts of thingamajigs! [whispers] Just don't tell Cocktail Peanut, nor Morpheus about it! [laughs] Features Live Free or GPU: Drama 3 normally wants a 45+ GB GPU. Here its experts move into system RAM, so the full model runs faster than real time on a single 32 GB card, and still fits on 24 GB. Rise and Shine, Dr. 16gb Cards: drama3-small (Japanese and English) also runs using same app, with no extra tools to install. On October 17th, Thomas will introduce the Drama 3 to the Macintosh. And you’ll see why "Drama 3" won’t be like "1984.": MLX 4-bit (64 GB) and 3-bit (48 GB) versions for Macs. It hasn't been tested on a Mac yet, so brave Mac owners, please report back! Be your own Scorcese: Clone a voice from a short clip, describe a new one, or let it surprise you. Then retake any line with a director's note like "angrier, faster" and add cues such as [sighs], [pause] or [laughs] right in the text. [whispers] Now I drank too much Cocktail Peanut. Push it to the limit: Paste a whole chapter. Long scripts are split into chunks, each read knowing what comes next, with natural pauses between them. [laughs] Nobody will see it coming! Be Marcia Lucas during the editing of Star Wars: Change a word and press Retake. Only the changed words are regenerated, and the rest of the take stays exactly as it was. The same type of stuff that can win an Oscar! No more Mr. Nice Guy: A pause fix and a stall guard stop the model from going silent or droning forever. Examples Cloning Donald Trump's voice: Cloning Gman in Half Life 1 (Low Quality 1990's audio to stress the model): Cloning Roy Marsden from my favorite TV Show The Sandbaggers (noisy 1970s tv audio): Cloning Darth Vader:
1.0.10 — validated audio, checkpoint and score-renderer updatesThis release updates the validated audio, checkpoint and score-rendering dependencies while keeping the model runtime and UI on tested versions. SoundFile 0.14.0 fixes concurrent file-open behavior; safetensors 0.8.0 updates checkpoint handling; abcjs 6.7.1 updates score rendering. Ruff is now 0.17.1 for development checks. Gradio is fixed at 6.17.3 to keep the tested UI / Hub combination. Installations using a later Gradio return to 6.17.3. The companion Pinokio installer now respects the app requirement when repairing Gradio assets. macOS retains Torch 2.14.1; Linux/CUDA retains 2.10.0. YuE2, yue2.cpp, model weights, NumPy, Transformers, Hub, Accelerate, tiktoken and the separate Cover environment are unchanged. Upgrade: Pinokio users click Update, then Start. No Reset is required. CLI users should use the platform override file as documented; the intentional upstream metadata mismatches for Torch (macOS), SoundFile and safetensors should not be “fixed” by reinstalling the older pins. Validation: 309 tests passed in both an upgraded environment and a clean installation, with lint, formatting, type and syntax checks passing. Native audio/checkpoint and UI construction checks passed on macOS, Linux and Windows. A bounded 1,280-token BF16/MPS generation produced 51.2 seconds of audio; tokens, latents and audio were byte-identical to the baseline. Desktop/mobile score screenshots were pixel-identical to the old renderer, and all seven Studio tabs opened without page errors. Full CUDA generation was not rerun; this is not a claim of complete cross-platform model equivalence.
Plaud + a self-hosted transcriber + Claude skill + PM CodexHi all! I wanted to share the bigger setup of my project management workflow using PM Codex, since a few pieces might be useful to people here even if you never touch PM Codex. The problem I have 1–3 hours of meetings a day. They’re bilingual, full of jargon, and people talk over each other. I record them on a Plaud Note Pro and Plaud Desktop, but I didn’t want Plaud’s subscription because I'm cheap. My local GPU was too weak to transcribe well, so I built my own pipeline in the cloud. Plaud Note Pro raw audio │ ▼ Claude scheduled task finds new recordings, submits them │ ▼ Cloud Transcriber (Modal) diarize · transcribe · name speakers │ ▼ Auricle (Claude) clean up · confirm speakers · draft MoM │ ▼ PM Codex change request to be reviewed 1. Plaud: raw audio only The official Plaud connector fetches the recording file. I don’t use any of Plaud’s paid transcription or summary features. 2. Claude scheduled task: hands-free pickup A Claude scheduled task runs in the cloud every hour during working hours on weekdays, so my computer doesn’t need to be on. Each run: Looks through my Plaud recordings from the last few days. Skips anything too short to be a meeting, anything unusually long, and anything that’s already been sent. Sends the remaining recordings to the transcriber and doesn’t wait for them to finish. Cleans up old transcription jobs. Reports back in one to three lines. It’s deliberately simple. It never asks questions, never touches PM Codex, and never guesses who’s speaking. It runs on a small, fast Claude model with automatic approval, so it costs almost nothing. By the time I sit down to review a meeting, the transcript is usually already waiting. 3. Cloud Transcriber: diarized ASR on Modal It runs on serverless GPUs that are billed per second and scale to zero. For each recording: pyannote works out who spoke when. Whisper large-v3, Qwen3-ASR and Qwen3-Omni (on vLLM) each transcribe every speaker turn. An ensemble keeps, for each turn, the version the engines agree on most. It flags likely hallucinations instead of silently deleting them. Voiceprints name the speakers. A speaker is named automatically only when the match is strong and clearly ahead of the next candidate. Otherwise it asks me. It costs about $0.25 per audio hour, which fits inside Modal’s free monthly credit for my usage. Before each job it checks how much credit is left and switches to a free fallback if there isn’t enough. 4. Auricle: the workflow layer (a Claude skill) When I say “Run auricle for a meeting at 2PM”, Claude picks up the finished transcript, or starts one if the scheduled task hasn’t picked up that recording yet. It cleans up the transcript using a glossary and the attendee list pulled from PM Codex. This fixes misheard names and acronyms. It asks me only about speakers it couldn’t identify, and shows example quotes so I can tell who’s who. It learns from my answers: confirmed speakers become voiceprints, and corrected terms go into the glossary. 5. Into PM Codex Auricle doesn’t write anything directly. It files one change request through the Claude bridge with the minutes, new tasks, risks and timeline updates. I review it and sign off in the app like any other change. What I learned Context beats model size. Giving the engines and the cleanup pass a glossary and attendee list did more than swapping engines. In my tests, the cleanup pass alone cut word errors by 3–6 points and recovered 20–35% more key terms. Combining engines beats any single one. My ensemble beat every engine on its own, and it also beat Plaud’s built-in transcript on the same audio. Crowded rooms are still hard. Every engine struggles with who said what when people talk over each other. Voiceprints are good for naming speakers but don’t fix that. Voiceprints are biometric data. If you build something similar, check your local privacy rules and make it easy for people to delete theirs. The transcriber and the Auricle skill aren’t public yet, but I’m happy to answer questions about the approach. If you’ve wired PM Codex into your own capture or transcription tools, I’d love to hear how!
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